Eat less salt, drink more wine, dump the cellphone, eat more salt, and live longer: teaching students to understand the role of data collection in statistical.

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Presentation on theme: "Eat less salt, drink more wine, dump the cellphone, eat more salt, and live longer: teaching students to understand the role of data collection in statistical."— Presentation transcript:

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Eat less salt, drink more wine, dump the cellphone, eat more salt, and live longer: teaching students to understand the role of data collection in statistical inference

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Outline 1) The problem. 2) Why is causal inference important? What is it? 3) Three types of data collection schemes. 4) What should students know? 5) What should we teach?

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The problem Statistical inference is concerned with answering the question “Could observed differences be simply due to chance?” To answer, we must choose an appropriate test or inferential procedure, check assumptions, calculate (or have calculated) appropriate test statistics, and compare results to an appropriate chance model.

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The problem This is familiar ground, because a background in mathematics prepares us well. But “is this due to chance” is only half the battle. The more interesting half is “What does this tell us about the world?”

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The problem And to answer this, we need to understand that data are not just numbers but, as statistician David Moore said: Data are numbers with context. And by “context” we usually mean the context in which the data were gathered.

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data collection and causal inference Statistical inference is limited or enhanced by the design of the data collection procedure. An important part of statistical inference is causal inference: X and Y might be associated, but can we also conclude that changing X causes a change in Y?

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What is causality? If a change in the value of x tends to result in a change in the value of y, then we say x causes y. For example, if I change my status from “no flu shot” to “flu shot”, then I am less likely to get the flu. Note that I could still get the flu, without invalidating the effectiveness of the vaccine.

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paradigm Two groups: Treatment and Control. Membership in these groups is recorded in a treatment variable. Response variable compared across the two groups. (But really, there can be multiple “treatments” and multiple “controls”.)

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Why Causal Inference is Hard Confounding Factors “Confounding means a difference between the treatment and control groups -- other than the treatment-- which affects the response” --David Freedman (Statistical Models)

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Fact of Life One can never know if there are confounding factors in an observational study. Researchers can eliminate, but there could always be a nagging doubt that we just haven’t thought of the right one.

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The secret to successful causation Make sure the groups you are comparing are similar in every way except for the value of the treatment variable. Said differently: make sure there are no confounding factors. Easier said than done. But here's one way of doing:

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Randomized, Controlled Experiments Random assignment assures similar groups. If sample sizes are large, both groups will be “the same”, thus eliminating confounders. If sample sizes are not large, both groups are the same “on average”.

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Randomized Only in a randomized, controlled experiment can we make causal inference. (and even then, we need to be aware of problems).

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Why? Because only in a randomized, controlled experiment are the treatment and control groups the same, on average. Thus, if the response variable is different the only explanation is the treatment.

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What every student should know & understand Data beat anecdotes. Random assignment in comparative experiments allows causal conclusions to be drawn. Random sampling allows generalization to the population Guidelines for Assessments and Instruction in Statistics Education (GAISE), College Report

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Random assignment No Random Assignment Random Sample causality can be extended to the population No causality, but an association can be extended to population. No Random Sample Causality, but only for the sample. no statistical inference Statistical Sleuth, Ramsey & Schafer

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Students should recognize Common sources of bias in surveys and experiments. How to determine when cause-and- effect can be inferred, based on how data were collected. Guidelines for Assessments and Instruction in Statistics Education (GAISE), College Report

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Students should know How to critique news stories and journal articles that include statistical information, including identifying what’s missing in the presentation and the flaws in the studies or methods used to generate the information. Guidelines for Assessments and Instruction in Statistics Education (GAISE), College Report

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What to Teach How to distinguish observational studies from controlled experiments. How to identify confounders and explain why they confound. Don’t rush to conclusions based on a single study. Controlled, randomized experiments can go wrong.

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What to Teach Give students headlines. Ask them whether the headline is making a claim for causation or for association. Students have difficulty telling these apart because our everyday language blurs the distinction.

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homeopathy The claim: Hyland’s Cold ‘n Cough 4 Kids will allow children to get over colds faster or prevent colds. Solution so heavily diluted that there are no molecules of active ingredients in the solution. Los Angeles Times, 12/6/2010

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homoepathy Proponents say that solution retains a “memory” of active ingredients. But as [Prof. Gleason, chemistry] explains, every molecule of water in our bodies has been enough other places -- oceans, sewers--to make any “memories” hopeless jumbled.

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homoepathy So even if a randomized, controlled study concluded that there was a benefit to the Cold ‘n Cough, Prof. Gleason would need to see more, because if the study was true, then many things we have longed believed about chemistry are false.

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Questions for students What is the research question? What is their answer? How were data collected? Are conclusions appropriate for data collection methods? To what population, if any, do conclusions apply? Have results been replicated? Or are they “amazing”? Gould & Ryan, 2012